The third paper from my PhD is soon to be published! It is very satisfying to see this particular chapter in (early view) print!

At an early stage of my PhD, Peter Vesk and I spent a few confusing hours attempting to conduct a ‘power analysis’ for our multi-species trait-based non-linear hierarchical growth model, but to no avail. Turns out, it just isn’t quite that easy. This led to some pretty extreme note taking during my many months of fieldwork in Murray Sunset National Park – where I made sure to collect information relating to the process behind collecting height-growth of multiple species in this semi-arid landscape.

Using this information and powered by an extreme determination from this field ecologist to ‘model well’, Cindy Hauser and I spent many solid hours together over about two years crafting a simulation of monstrous proportions. We attempted to simulate my entire fieldwork process, subset this simulated data under various constraining scenarios, analysis all our scenario-driven datasets and evaluate how particular decisions made in the field would effect the precision, accuracy and bias of our modelled growth parameters.

The journey was long and contained many dimensions, so it is lovely to see this project in finally in print!

A colourful schematic of part of our simulation

Abstract

Field data collection can be expensive, time consuming and difficult; insightful research requires statistical analyses supported by sufficient data. Pilot studies and power analysis provide guidance on sampling design but can be challenging to perform, as ecologists increasingly collect multiple types of data over different scales. Despite a growing simulation literature, it remains unclear how to appropriately design data collection for many complex projects.Approaches that seek to achieve realism in decision-making contexts, such as management strategy evaluation and virtual ecologist simulations, can help.

For a relatively complex analysis, we develop and demonstrate a flexible simulation approach that informs what data are needed and how long those data will take to collect, under realistic fieldwork constraints. We simulated data collection and analysis under different constraint scenarios that varied in deterministic (field trip length, travel and measurement times) and stochastic (species detection and occupancy rates, and inclement weather) features. In our case study, we fit plant height data to a multi-species, three-parameter nonlinear growth model. We tested how the simulated datasets, based on the varying constraint scenarios, affected the model fit (parameter bias, uncertainty and capture rate). Species prevalence in the field exerted a stronger influence on the datasets and downstream model performance than deterministic aspects such as travel times. When species detection and occupancy were not considered, the field time needed to collect an adequate dataset was underestimated by 40%.

Simulations can assist in refining fieldwork design, estimating field costs and incorporating uncertainties into project planning. We argue that combining data collection, analysis and decision-making processes in a flexible virtual setting can help address many of the decisions that field ecologists face when designing field-based research.

The paper is available here.
Please get in touch if you have any questions / comments!